We present, in this paper, our contribution in SemEval2017 task 4 : " Sentiment Analysis in Twitter " , subtask A: " Message Polarity Classification " , for En-glish and Arabic languages. Our system is based on a list of sentiment seed words adapted for tweets. The sentiment relations between seed words and other terms are captured by cosine similarity between the word embedding representations (word2vec). These seed words are extracted from datasets of annotated tweets available online. Our tests, using these seed words, show significant improvement in results compared to the use of Turney and Littman's (2003) seed words, on polarity classification of tweet messages.